Abstract
Unexpected failures of industrial gearboxes may cause significant economic losses. It is therefore important to detect early fault symptoms. This paper introduces signal processing methods based on approximate entropy (ApEn) and Lempel-Ziv Complexity (LZC) for defect detection of gears. Both methods are statistical measurements exploring the regularity of a vibratory signal. Applied to gear signals, the parameter selection of ApEn and LZC calculation are first numerically investigated, and appropriate parameters are suggested. Finally, an experimental study is presented to investigate the effectiveness of these indicators. The results demonstrate that ApEn and LZC provide alternative features for signal processing. A new methodology is presented combining both Kurtosis and LZC for early detection of faults. The results show that this proposed method may be used as an effective tool for early detection of gear faults.
Highlights
Gearboxes play an important role in industrial applications, and unexpected failures often result in significant economic losses
Compared to classical techniques such as statistical time indicators or Fast Fourier Transform, advanced signal processing techniques like time-frequency analysis (STFT, Wigner-Ville) [1,2,3,4] or wavelet transform [5, 6] have shown to be more efficient for gear defect detection
In this paper, the approximate entropy (ApEn) and Lempel-Ziv complexity (LZC) methods are compared in order to analyse vibration signals from gear and investigate their efficiency for the defect detection and severity evaluation of gears faults
Summary
Gearboxes play an important role in industrial applications, and unexpected failures often result in significant economic losses. The Lempel-Ziv complexity (LZC) and approximate entropy (ApEn) methods present alternative tools for signal analysis involving nonlinear dynamics. These methods are becoming popular and have found wide applications in various disciplines, especially in the field of biomedical engineering. Yan and Gao [16] investigated the application of Lemp-Ziv complexity (LZC) for the health monitoring of rolling element bearings. In this paper, the ApEn and LZC methods are compared in order to analyse vibration signals from gear and investigate their efficiency for the defect detection and severity evaluation of gears faults
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